Detecting depression tendency with multimodal features.

• Our MTDD model is an integrated knowledge-driven and data-driven model. This method avoids problems such as not considering expert experience or insight, not paying attention to the overall situation, and lack of interpretability brought about by only using data-driven technology. It not only utilizes the text features and the semantic features but also applies the domain knowledge to learn the representation of the depression tendency, thus making the model more robust. In other words, the model combines text features, semantic features, and domain knowledge. The Word2Vec word embedding integrates the emotional information of the words in the emotional dictionary, expands the existing emotional dictionary, extracts the TF-IDF word frequency feature, and proposes seven grammatical analysis rules to obtain the text emotional value feature making it more suitable for depression tendency detection classification task. • The MTDD model is a deep neural network hybrid model, which circumvents the weak generalization ability of a single model for identifying depression tendencies. Specifically, the MTDD model combines the advantages of CNN and Bi-LSTM networks. The CNN network offers the advantage of the ability to extract the local features of the text. In addition, BiLSTM can effectively capture bidirectional semantic information. This combination can better represent text features and improve the model's classification accuracy. • Our MTDD model is obtained based on real data, thus making the model more suitable for practical problems. As far as we know, many existing depression detection methods are only trained on some experimental data sets, so the model's generalization ability is limited and cannot even be applied in realistic scenes. In comparison, the MTDD model is trained on social platform data, making the data more objective and accurate. In addition, the data of social platforms can be obtained at a low cost. It is easy to operate and does not require a lot of laborious labeling. Moreover, our approach avoids the influence of subjective factors in the method of consultation by mental health experts and the influence of non-public and imperfect data used for depression. • We conducted extensive experiments on a Reddit data set and a Twitter data set. The results show that, compared to multiple latest depression detection models, our MTDD model detects users who may be depressed with a 95% F1 value and obtains SOTA results. [Display omitted] Background and Objective: Depression can severely impact physical and mental health and may even harm society. Therefore, detecting the early symptoms of depression and treating them on time is critical. The widespread use of social media has led individuals with depressive tendencies to express their emotions on social platforms, share their painful experiences, and seek support and help. Therefore, the massive available amounts of social platform data provide the possibility of identifying depressive tendencies. Methods: This paper proposes a neural network hybrid model MTDD to achieve this goal. Analysis of the content of users' posts on social platforms has facilitated constructing a post-level method to detect depressive tendencies in individuals. Compared with existing methods, the MTDD model uses the following innovative methods: First, this model is based on social platform data, which is objective and accurate, can be obtained at a low cost, and is easy to operate. The model can avoid the influence of subjective factors in the depressive tendency detection method based on consultation with mental health experts. In other words, it can avoid the problem of undisclosed and imperfect data in depressive tendency detection. Second, the MTDD model is based on a deep neural network hybrid model, combining the advantages of CNN and BiLSTM networks and avoiding the problem of poor generalization ability in a single model for depression tendency recognition. Third, the MTDD model is based on multimodal features for learning the vector representation of depression-prone text, including text features, semantic features, and domain knowledge, making the model more robust. Results: Extensive experimental results demonstrate that our MTDD model detects users who may have a depressive tendency with a 95 % F1 value and obtained SOTA results. Conclusions: Our MTDD model can detect depressive users on social media platforms more effectively, providing the possibility for early diagnosis and timely treatment of depression. The experiment proves that our MTDD model outperforms many of the latest depressive tendency detection models. [ABSTRACT FROM AUTHOR]